Overview

Brought to you by YData

Dataset statistics

Number of variables37
Number of observations97152
Missing cells110593
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.2 MiB
Average record size in memory304.0 B

Variable types

Text9
Numeric11
Categorical9
DateTime6
Boolean1
Unsupported1

Alerts

Estimated Cost is highly overall correlated with Revised CostHigh correlation
Existing Construction Type is highly overall correlated with Existing Construction Type Description and 2 other fieldsHigh correlation
Existing Construction Type Description is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Existing Units is highly overall correlated with Proposed UnitsHigh correlation
Neighborhoods - Analysis Boundaries is highly overall correlated with ZipcodeHigh correlation
Number of Existing Stories is highly overall correlated with Number of Proposed StoriesHigh correlation
Number of Proposed Stories is highly overall correlated with Number of Existing StoriesHigh correlation
Permit Type is highly overall correlated with Permit Type DefinitionHigh correlation
Permit Type Definition is highly overall correlated with Permit TypeHigh correlation
Proposed Construction Type is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Proposed Construction Type Description is highly overall correlated with Existing Construction Type and 2 other fieldsHigh correlation
Proposed Units is highly overall correlated with Existing UnitsHigh correlation
Revised Cost is highly overall correlated with Estimated CostHigh correlation
Zipcode is highly overall correlated with Neighborhoods - Analysis BoundariesHigh correlation
Permit Type Definition is highly imbalanced (85.2%)Imbalance
Street Suffix is highly imbalanced (68.3%)Imbalance
Current Status is highly imbalanced (99.5%)Imbalance
Plansets is highly imbalanced (57.3%)Imbalance
Unit has 82372 (84.8%) missing valuesMissing
Number of Existing Stories has 1892 (1.9%) missing valuesMissing
Number of Proposed Stories has 1654 (1.7%) missing valuesMissing
Existing Units has 6971 (7.2%) missing valuesMissing
Proposed Units has 6562 (6.8%) missing valuesMissing
Existing Construction Type has 2226 (2.3%) missing valuesMissing
Existing Construction Type Description has 2226 (2.3%) missing valuesMissing
Proposed Construction Type has 1811 (1.9%) missing valuesMissing
Proposed Construction Type Description has 1811 (1.9%) missing valuesMissing
Estimated Cost is highly skewed (γ1 = 77.30022601)Skewed
Revised Cost is highly skewed (γ1 = 75.01904853)Skewed
Supervisor District is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unit has 10489 (10.8%) zerosZeros
Existing Units has 18058 (18.6%) zerosZeros
Proposed Units has 17174 (17.7%) zerosZeros

Reproduction

Analysis started2024-08-30 11:57:43.341825
Analysis finished2024-08-30 11:58:21.171692
Duration37.83 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Distinct88898
Distinct (%)91.5%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:21.470444image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.999897
Min length7

Characters and Unicode

Total characters1165814
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81979 ?
Unique (%)84.4%

Sample

1st row201305318356
2nd row201410279983
3rd row201307161925
4th row201404113025
5th row201702109134
ValueCountFrequency (%)
201602179758 66
 
0.1%
201602179775 30
 
< 0.1%
201708165004 9
 
< 0.1%
201702239990 9
 
< 0.1%
201707061162 8
 
< 0.1%
201602179774 8
 
< 0.1%
201702099112 8
 
< 0.1%
201604285989 8
 
< 0.1%
201711284975 7
 
< 0.1%
201705267761 7
 
< 0.1%
Other values (88888) 96992
99.8%
2024-08-30T08:58:21.916419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 257569
22.1%
1 216089
18.5%
2 191378
16.4%
3 83406
 
7.2%
5 78787
 
6.8%
4 78016
 
6.7%
6 75701
 
6.5%
7 70465
 
6.0%
8 57731
 
5.0%
9 56670
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1165812
> 99.9%
Uppercase Letter 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 257569
22.1%
1 216089
18.5%
2 191378
16.4%
3 83406
 
7.2%
5 78787
 
6.8%
4 78016
 
6.7%
6 75701
 
6.5%
7 70465
 
6.0%
8 57731
 
5.0%
9 56670
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
M 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1165812
> 99.9%
Latin 2
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 257569
22.1%
1 216089
18.5%
2 191378
16.4%
3 83406
 
7.2%
5 78787
 
6.8%
4 78016
 
6.7%
6 75701
 
6.5%
7 70465
 
6.0%
8 57731
 
5.0%
9 56670
 
4.9%
Latin
ValueCountFrequency (%)
M 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1165814
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 257569
22.1%
1 216089
18.5%
2 191378
16.4%
3 83406
 
7.2%
5 78787
 
6.8%
4 78016
 
6.7%
6 75701
 
6.5%
7 70465
 
6.0%
8 57731
 
5.0%
9 56670
 
4.9%

Permit Type
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6743351
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:22.043690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median8
Q38
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.220117
Coefficient of variation (CV)0.15898667
Kurtosis10.773969
Mean7.6743351
Median Absolute Deviation (MAD)0
Skewness-3.5459275
Sum745577
Variance1.4886855
MonotonicityNot monotonic
2024-08-30T08:58:22.155389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
8 90502
93.2%
3 5013
 
5.2%
4 976
 
1.0%
2 305
 
0.3%
6 170
 
0.2%
7 103
 
0.1%
5 46
 
< 0.1%
1 37
 
< 0.1%
ValueCountFrequency (%)
1 37
 
< 0.1%
2 305
 
0.3%
3 5013
 
5.2%
4 976
 
1.0%
5 46
 
< 0.1%
6 170
 
0.2%
7 103
 
0.1%
8 90502
93.2%
ValueCountFrequency (%)
8 90502
93.2%
7 103
 
0.1%
6 170
 
0.2%
5 46
 
< 0.1%
4 976
 
1.0%
3 5013
 
5.2%
2 305
 
0.3%
1 37
 
< 0.1%

Permit Type Definition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
otc alterations permit
90502 
additions alterations or repairs
 
5013
sign - erect
 
976
new construction wood frame
 
305
demolitions
 
170
Other values (3)
 
186

Length

Max length35
Median length22
Mean length22.413733
Min length11

Characters and Unicode

Total characters2177539
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowotc alterations permit
2nd rowotc alterations permit
3rd rowotc alterations permit
4th rowotc alterations permit
5th rowotc alterations permit

Common Values

ValueCountFrequency (%)
otc alterations permit 90502
93.2%
additions alterations or repairs 5013
 
5.2%
sign - erect 976
 
1.0%
new construction wood frame 305
 
0.3%
demolitions 170
 
0.2%
wall or painted sign 103
 
0.1%
grade or quarry or fill or excavate 46
 
< 0.1%
new construction 37
 
< 0.1%

Length

2024-08-30T08:58:22.290062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:22.416835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
alterations 95515
32.2%
otc 90502
30.5%
permit 90502
30.5%
or 5254
 
1.8%
additions 5013
 
1.7%
repairs 5013
 
1.7%
sign 1079
 
0.4%
976
 
0.3%
erect 976
 
0.3%
new 342
 
0.1%
Other values (10) 1512
 
0.5%

Most occurring characters

ValueCountFrequency (%)
t 379026
17.4%
r 203058
9.3%
i 202966
9.3%
a 201751
9.3%
199532
9.2%
o 197918
9.1%
e 194040
8.9%
s 107132
 
4.9%
n 102906
 
4.7%
l 95983
 
4.4%
Other values (13) 293227
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1977031
90.8%
Space Separator 199532
 
9.2%
Dash Punctuation 976
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 379026
19.2%
r 203058
10.3%
i 202966
10.3%
a 201751
10.2%
o 197918
10.0%
e 194040
9.8%
s 107132
 
5.4%
n 102906
 
5.2%
l 95983
 
4.9%
p 95618
 
4.8%
Other values (11) 196633
9.9%
Space Separator
ValueCountFrequency (%)
199532
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 976
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1977031
90.8%
Common 200508
 
9.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 379026
19.2%
r 203058
10.3%
i 202966
10.3%
a 201751
10.2%
o 197918
10.0%
e 194040
9.8%
s 107132
 
5.4%
n 102906
 
5.2%
l 95983
 
4.9%
p 95618
 
4.8%
Other values (11) 196633
9.9%
Common
ValueCountFrequency (%)
199532
99.5%
- 976
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2177539
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 379026
17.4%
r 203058
9.3%
i 202966
9.3%
a 201751
9.3%
199532
9.2%
o 197918
9.1%
e 194040
8.9%
s 107132
 
4.9%
n 102906
 
4.7%
l 95983
 
4.4%
Other values (13) 293227
13.5%
Distinct1289
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2012-03-28 00:00:00
Maximum2018-02-22 00:00:00
2024-08-30T08:58:22.586748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:22.761921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Block
Text

Distinct4716
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:23.153919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0394433
Min length4

Characters and Unicode

Total characters392440
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)0.2%

Sample

1st row1810
2nd row0661
3rd row3565
4th row3740
5th row0195
ValueCountFrequency (%)
3708 790
 
0.8%
3735 481
 
0.5%
0289 413
 
0.4%
3717 399
 
0.4%
3709 382
 
0.4%
0259 375
 
0.4%
3721 372
 
0.4%
7331 372
 
0.4%
3706 349
 
0.4%
3707 326
 
0.3%
Other values (4706) 92893
95.6%
2024-08-30T08:58:23.651870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59621
15.2%
3 48339
12.3%
1 46772
11.9%
2 43137
11.0%
5 37954
9.7%
7 37630
9.6%
6 37207
9.5%
4 28623
7.3%
8 25273
6.4%
9 24053
6.1%
Other values (9) 3831
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 388609
99.0%
Uppercase Letter 3831
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59621
15.3%
3 48339
12.4%
1 46772
12.0%
2 43137
11.1%
5 37954
9.8%
7 37630
9.7%
6 37207
9.6%
4 28623
7.4%
8 25273
6.5%
9 24053
6.2%
Uppercase Letter
ValueCountFrequency (%)
A 2670
69.7%
B 492
 
12.8%
C 448
 
11.7%
D 69
 
1.8%
Z 61
 
1.6%
F 46
 
1.2%
E 36
 
0.9%
T 7
 
0.2%
G 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 388609
99.0%
Latin 3831
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59621
15.3%
3 48339
12.4%
1 46772
12.0%
2 43137
11.1%
5 37954
9.8%
7 37630
9.7%
6 37207
9.6%
4 28623
7.4%
8 25273
6.5%
9 24053
6.2%
Latin
ValueCountFrequency (%)
A 2670
69.7%
B 492
 
12.8%
C 448
 
11.7%
D 69
 
1.8%
Z 61
 
1.6%
F 46
 
1.2%
E 36
 
0.9%
T 7
 
0.2%
G 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59621
15.2%
3 48339
12.3%
1 46772
11.9%
2 43137
11.0%
5 37954
9.7%
7 37630
9.6%
6 37207
9.5%
4 28623
7.3%
8 25273
6.4%
9 24053
6.1%
Other values (9) 3831
 
1.0%

Lot
Text

Distinct917
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:24.027439image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0806468
Min length3

Characters and Unicode

Total characters299291
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)0.2%

Sample

1st row017A
2nd row005
3rd row076
4th row029
5th row001
ValueCountFrequency (%)
001 4780
 
4.9%
007 2516
 
2.6%
008 2441
 
2.5%
006 2379
 
2.4%
002 2322
 
2.4%
003 2293
 
2.4%
005 2244
 
2.3%
009 2121
 
2.2%
021 2111
 
2.2%
004 2089
 
2.2%
Other values (907) 71856
74.0%
2024-08-30T08:58:24.519269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 129587
43.3%
1 38712
 
12.9%
2 29546
 
9.9%
3 20919
 
7.0%
4 16236
 
5.4%
5 13299
 
4.4%
6 12441
 
4.2%
7 11042
 
3.7%
8 10099
 
3.4%
9 9581
 
3.2%
Other values (26) 7829
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 291462
97.4%
Uppercase Letter 7829
 
2.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3786
48.4%
B 1394
 
17.8%
C 707
 
9.0%
D 466
 
6.0%
E 307
 
3.9%
F 232
 
3.0%
G 191
 
2.4%
I 106
 
1.4%
J 105
 
1.3%
H 104
 
1.3%
Other values (16) 431
 
5.5%
Decimal Number
ValueCountFrequency (%)
0 129587
44.5%
1 38712
 
13.3%
2 29546
 
10.1%
3 20919
 
7.2%
4 16236
 
5.6%
5 13299
 
4.6%
6 12441
 
4.3%
7 11042
 
3.8%
8 10099
 
3.5%
9 9581
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 291462
97.4%
Latin 7829
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3786
48.4%
B 1394
 
17.8%
C 707
 
9.0%
D 466
 
6.0%
E 307
 
3.9%
F 232
 
3.0%
G 191
 
2.4%
I 106
 
1.4%
J 105
 
1.3%
H 104
 
1.3%
Other values (16) 431
 
5.5%
Common
ValueCountFrequency (%)
0 129587
44.5%
1 38712
 
13.3%
2 29546
 
10.1%
3 20919
 
7.2%
4 16236
 
5.6%
5 13299
 
4.6%
6 12441
 
4.3%
7 11042
 
3.8%
8 10099
 
3.5%
9 9581
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 299291
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 129587
43.3%
1 38712
 
12.9%
2 29546
 
9.9%
3 20919
 
7.0%
4 16236
 
5.4%
5 13299
 
4.4%
6 12441
 
4.2%
7 11042
 
3.7%
8 10099
 
3.4%
9 9581
 
3.2%
Other values (26) 7829
 
2.6%

Street Number
Real number (ℝ)

Distinct4702
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1073.8915
Minimum1
Maximum8400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:24.674259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29
Q1219
median650
Q31619
95-th percentile3395
Maximum8400
Range8399
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation1121.1989
Coefficient of variation (CV)1.0440524
Kurtosis2.5992423
Mean1073.8915
Median Absolute Deviation (MAD)539
Skewness1.5247697
Sum1.043307 × 108
Variance1257087.1
MonotonicityNot monotonic
2024-08-30T08:58:24.819159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1391
 
1.4%
101 689
 
0.7%
100 656
 
0.7%
50 655
 
0.7%
201 629
 
0.6%
555 597
 
0.6%
2 457
 
0.5%
55 417
 
0.4%
600 379
 
0.4%
150 371
 
0.4%
Other values (4692) 90911
93.6%
ValueCountFrequency (%)
1 1391
1.4%
2 457
 
0.5%
3 173
 
0.2%
4 154
 
0.2%
5 89
 
0.1%
6 63
 
0.1%
7 65
 
0.1%
8 139
 
0.1%
9 63
 
0.1%
10 148
 
0.2%
ValueCountFrequency (%)
8400 3
< 0.1%
8331 1
 
< 0.1%
8325 3
< 0.1%
8320 1
 
< 0.1%
8300 1
 
< 0.1%
8245 1
 
< 0.1%
8228 1
 
< 0.1%
8222 2
< 0.1%
8219 1
 
< 0.1%
8200 1
 
< 0.1%
Distinct1578
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:25.166685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length25
Median length22
Mean length6.5469059
Min length3

Characters and Unicode

Total characters636045
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique116 ?
Unique (%)0.1%

Sample

1st row43rD
2nd rowbUsH
3rd row16tH
4th rowfOlSoM
5th rowmOnTgOmErY
ValueCountFrequency (%)
market 3107
 
3.0%
california 2620
 
2.5%
mission 2074
 
2.0%
montgomery 1767
 
1.7%
20th 959
 
0.9%
03rd 929
 
0.9%
geary 909
 
0.9%
pine 831
 
0.8%
post 781
 
0.7%
folsom 762
 
0.7%
Other values (1571) 89455
85.9%
2024-08-30T08:58:25.654288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 41831
 
6.6%
O 31090
 
4.9%
e 28234
 
4.4%
r 27733
 
4.4%
n 27600
 
4.3%
E 26220
 
4.1%
t 25542
 
4.0%
s 24001
 
3.8%
R 21729
 
3.4%
T 21451
 
3.4%
Other values (55) 360614
56.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 316394
49.7%
Uppercase Letter 277533
43.6%
Decimal Number 34974
 
5.5%
Space Separator 7050
 
1.1%
Other Punctuation 94
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 41831
15.1%
O 31090
11.2%
E 26220
9.4%
R 21729
 
7.8%
T 21451
 
7.7%
N 20280
 
7.3%
H 18459
 
6.7%
I 18354
 
6.6%
L 11588
 
4.2%
S 10995
 
4.0%
Other values (16) 55536
20.0%
Lowercase Letter
ValueCountFrequency (%)
e 28234
 
8.9%
r 27733
 
8.8%
n 27600
 
8.7%
t 25542
 
8.1%
s 24001
 
7.6%
l 20024
 
6.3%
a 19865
 
6.3%
o 16970
 
5.4%
m 15914
 
5.0%
c 15876
 
5.0%
Other values (16) 94635
29.9%
Decimal Number
ValueCountFrequency (%)
2 7294
20.9%
1 5819
16.6%
0 5521
15.8%
3 3937
11.3%
4 3739
10.7%
5 1869
 
5.3%
6 1796
 
5.1%
7 1739
 
5.0%
8 1732
 
5.0%
9 1528
 
4.4%
Other Punctuation
ValueCountFrequency (%)
. 59
62.8%
' 35
37.2%
Space Separator
ValueCountFrequency (%)
7050
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 593927
93.4%
Common 42118
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 41831
 
7.0%
O 31090
 
5.2%
e 28234
 
4.8%
r 27733
 
4.7%
n 27600
 
4.6%
E 26220
 
4.4%
t 25542
 
4.3%
s 24001
 
4.0%
R 21729
 
3.7%
T 21451
 
3.6%
Other values (42) 318496
53.6%
Common
ValueCountFrequency (%)
2 7294
17.3%
7050
16.7%
1 5819
13.8%
0 5521
13.1%
3 3937
9.3%
4 3739
8.9%
5 1869
 
4.4%
6 1796
 
4.3%
7 1739
 
4.1%
8 1732
 
4.1%
Other values (3) 1622
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 636045
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 41831
 
6.6%
O 31090
 
4.9%
e 28234
 
4.4%
r 27733
 
4.4%
n 27600
 
4.3%
E 26220
 
4.1%
t 25542
 
4.0%
s 24001
 
3.8%
R 21729
 
3.4%
T 21451
 
3.4%
Other values (55) 360614
56.7%

Street Suffix
Categorical

IMBALANCE 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
St
67798 
Av
21803 
Wy
 
1917
Dr
 
1842
Bl
 
1749
Other values (14)
 
2043

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters194304
Distinct characters23
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAv
2nd rowSt
3rd rowSt
4th rowSt
5th rowSt

Common Values

ValueCountFrequency (%)
St 67798
69.8%
Av 21803
 
22.4%
Wy 1917
 
2.0%
Dr 1842
 
1.9%
Bl 1749
 
1.8%
Tr 743
 
0.8%
Ct 417
 
0.4%
Pl 240
 
0.2%
Ln 183
 
0.2%
Pz 116
 
0.1%
Other values (9) 344
 
0.4%

Length

2024-08-30T08:58:25.794903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st 67798
69.8%
av 21803
 
22.4%
wy 1917
 
2.0%
dr 1842
 
1.9%
bl 1749
 
1.8%
tr 743
 
0.8%
ct 417
 
0.4%
pl 240
 
0.2%
ln 183
 
0.2%
pz 116
 
0.1%
Other values (9) 344
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 68215
35.1%
S 67800
34.9%
A 21839
 
11.2%
v 21803
 
11.2%
r 2638
 
1.4%
y 2027
 
1.0%
l 2025
 
1.0%
W 1917
 
1.0%
D 1842
 
0.9%
B 1749
 
0.9%
Other values (13) 2449
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 97152
50.0%
Uppercase Letter 97152
50.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 67800
69.8%
A 21839
 
22.5%
W 1917
 
2.0%
D 1842
 
1.9%
B 1749
 
1.8%
T 743
 
0.8%
C 470
 
0.5%
P 416
 
0.4%
L 183
 
0.2%
H 110
 
0.1%
Other values (2) 83
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
t 68215
70.2%
v 21803
 
22.4%
r 2638
 
2.7%
y 2027
 
2.1%
l 2025
 
2.1%
n 183
 
0.2%
z 116
 
0.1%
d 78
 
0.1%
k 60
 
0.1%
w 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 194304
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 68215
35.1%
S 67800
34.9%
A 21839
 
11.2%
v 21803
 
11.2%
r 2638
 
1.4%
y 2027
 
1.0%
l 2025
 
1.0%
W 1917
 
1.0%
D 1842
 
0.9%
B 1749
 
0.9%
Other values (13) 2449
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 194304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 68215
35.1%
S 67800
34.9%
A 21839
 
11.2%
v 21803
 
11.2%
r 2638
 
1.4%
y 2027
 
1.0%
l 2025
 
1.0%
W 1917
 
1.0%
D 1842
 
0.9%
B 1749
 
0.9%
Other values (13) 2449
 
1.3%

Unit
Real number (ℝ)

MISSING  ZEROS 

Distinct529
Distinct (%)3.6%
Missing82372
Missing (%)84.8%
Infinite0
Infinite (%)0.0%
Mean83.109675
Minimum0
Maximum6003
Zeros10489
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:25.924443image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile504
Maximum6003
Range6003
Interquartile range (IQR)2

Descriptive statistics

Standard deviation312.38325
Coefficient of variation (CV)3.7586869
Kurtosis80.146784
Mean83.109675
Median Absolute Deviation (MAD)0
Skewness7.3730844
Sum1228361
Variance97583.293
MonotonicityNot monotonic
2024-08-30T08:58:26.074374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10489
 
10.8%
1 572
 
0.6%
2 311
 
0.3%
3 260
 
0.3%
101 151
 
0.2%
4 147
 
0.2%
5 110
 
0.1%
6 97
 
0.1%
201 94
 
0.1%
301 62
 
0.1%
Other values (519) 2487
 
2.6%
(Missing) 82372
84.8%
ValueCountFrequency (%)
0 10489
10.8%
1 572
 
0.6%
2 311
 
0.3%
3 260
 
0.3%
4 147
 
0.2%
5 110
 
0.1%
6 97
 
0.1%
7 52
 
0.1%
8 57
 
0.1%
9 42
 
< 0.1%
ValueCountFrequency (%)
6003 1
< 0.1%
5903 1
< 0.1%
5604 1
< 0.1%
5206 1
< 0.1%
5204 1
< 0.1%
5106 1
< 0.1%
4410 1
< 0.1%
4310 1
< 0.1%
4210 1
< 0.1%
4113 1
< 0.1%
Distinct80515
Distinct (%)82.9%
Missing7
Missing (%)< 0.1%
Memory size1.5 MiB
2024-08-30T08:58:26.422844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length300
Median length230
Mean length121.29319
Min length6

Characters and Unicode

Total characters11783027
Distinct characters70
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73753 ?
Unique (%)75.9%

Sample

1st rowremodel kitchen: replace countertop, cabinets, sink, stove, hardwood floor & lighting.
2nd rowtwo kitchens & two bathrooms remodel. replace kitchen cabinets, countertops & appliances in same location. replace tub & faucet & vanity & tile in same location in both units.
3rd rowto comply with physical inspection report #cc-7260 item #1 -all storage items are being removed.
4th rowexisting office and storage facility. new partitions, restroom upgrade. electrical on a separate permit
5th rowbasement & 1/f- as built drawing for fire/smoke damper on hvac duct (existing) under pa#201612074341. n/a ordinance #155-13
ValueCountFrequency (%)
to 55208
 
3.0%
54501
 
2.9%
and 43163
 
2.3%
new 37595
 
2.0%
of 31582
 
1.7%
replace 23834
 
1.3%
in 23158
 
1.2%
with 21404
 
1.1%
for 21082
 
1.1%
floor 20021
 
1.1%
Other values (73055) 1537591
82.3%
2024-08-30T08:58:26.938996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1796856
15.2%
e 969781
 
8.2%
o 772426
 
6.6%
r 736360
 
6.2%
n 694521
 
5.9%
t 677808
 
5.8%
i 668124
 
5.7%
a 662645
 
5.6%
l 477741
 
4.1%
s 455122
 
3.9%
Other values (60) 3871643
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8714474
74.0%
Space Separator 1796856
 
15.2%
Decimal Number 691927
 
5.9%
Other Punctuation 447125
 
3.8%
Dash Punctuation 56363
 
0.5%
Close Punctuation 37775
 
0.3%
Open Punctuation 36558
 
0.3%
Math Symbol 1767
 
< 0.1%
Currency Symbol 141
 
< 0.1%
Connector Punctuation 28
 
< 0.1%
Other values (3) 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 969781
11.1%
o 772426
 
8.9%
r 736360
 
8.4%
n 694521
 
8.0%
t 677808
 
7.8%
i 668124
 
7.7%
a 662645
 
7.6%
l 477741
 
5.5%
s 455122
 
5.2%
d 365726
 
4.2%
Other values (17) 2234220
25.6%
Other Punctuation
ValueCountFrequency (%)
. 181358
40.6%
, 116009
25.9%
& 35639
 
8.0%
# 34376
 
7.7%
/ 32350
 
7.2%
: 17075
 
3.8%
; 9298
 
2.1%
" 7394
 
1.7%
' 5363
 
1.2%
@ 3530
 
0.8%
Other values (5) 4733
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 144313
20.9%
0 122539
17.7%
2 119967
17.3%
3 64534
9.3%
5 57302
 
8.3%
4 51237
 
7.4%
6 39148
 
5.7%
7 33864
 
4.9%
8 31139
 
4.5%
9 27884
 
4.0%
Math Symbol
ValueCountFrequency (%)
< 632
35.8%
+ 532
30.1%
= 427
24.2%
> 143
 
8.1%
~ 33
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
- 56362
> 99.9%
– 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 37768
> 99.9%
] 7
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 36540
> 99.9%
[ 18
 
< 0.1%
Modifier Symbol
ValueCountFrequency (%)
^ 6
60.0%
` 4
40.0%
Space Separator
ValueCountFrequency (%)
1796856
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 141
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 28
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8714474
74.0%
Common 3068553
 
26.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1796856
58.6%
. 181358
 
5.9%
1 144313
 
4.7%
0 122539
 
4.0%
2 119967
 
3.9%
, 116009
 
3.8%
3 64534
 
2.1%
5 57302
 
1.9%
- 56362
 
1.8%
4 51237
 
1.7%
Other values (33) 358076
 
11.7%
Latin
ValueCountFrequency (%)
e 969781
11.1%
o 772426
 
8.9%
r 736360
 
8.4%
n 694521
 
8.0%
t 677808
 
7.8%
i 668124
 
7.7%
a 662645
 
7.6%
l 477741
 
5.5%
s 455122
 
5.2%
d 365726
 
4.2%
Other values (17) 2234220
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11782970
> 99.9%
None 55
 
< 0.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1796856
15.2%
e 969781
 
8.2%
o 772426
 
6.6%
r 736360
 
6.2%
n 694521
 
5.9%
t 677808
 
5.8%
i 668124
 
5.7%
a 662645
 
5.6%
l 477741
 
4.1%
s 455122
 
3.9%
Other values (56) 3871586
32.9%
None
ValueCountFrequency (%)
ç 53
96.4%
½ 2
 
3.6%
Punctuation
ValueCountFrequency (%)
– 1
50.0%
’ 1
50.0%

Current Status
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
complete
97040 
issued
 
52
reinstated
 
36
expired
 
7
cancelled
 
5
Other values (4)
 
12

Length

Max length10
Median length8
Mean length7.9995986
Min length6

Characters and Unicode

Total characters777177
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcomplete
2nd rowcomplete
3rd rowcomplete
4th rowcomplete
5th rowcomplete

Common Values

ValueCountFrequency (%)
complete 97040
99.9%
issued 52
 
0.1%
reinstated 36
 
< 0.1%
expired 7
 
< 0.1%
cancelled 5
 
< 0.1%
revoked 4
 
< 0.1%
approved 4
 
< 0.1%
suspend 3
 
< 0.1%
incomplete 1
 
< 0.1%

Length

2024-08-30T08:58:27.091291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:27.230383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
complete 97040
99.9%
issued 52
 
0.1%
reinstated 36
 
< 0.1%
expired 7
 
< 0.1%
cancelled 5
 
< 0.1%
revoked 4
 
< 0.1%
approved 4
 
< 0.1%
suspend 3
 
< 0.1%
incomplete 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 194245
25.0%
t 97113
12.5%
p 97059
12.5%
c 97051
12.5%
l 97051
12.5%
o 97049
12.5%
m 97041
12.5%
s 146
 
< 0.1%
d 111
 
< 0.1%
i 96
 
< 0.1%
Other values (7) 215
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 777177
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 194245
25.0%
t 97113
12.5%
p 97059
12.5%
c 97051
12.5%
l 97051
12.5%
o 97049
12.5%
m 97041
12.5%
s 146
 
< 0.1%
d 111
 
< 0.1%
i 96
 
< 0.1%
Other values (7) 215
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 777177
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 194245
25.0%
t 97113
12.5%
p 97059
12.5%
c 97051
12.5%
l 97051
12.5%
o 97049
12.5%
m 97041
12.5%
s 146
 
< 0.1%
d 111
 
< 0.1%
i 96
 
< 0.1%
Other values (7) 215
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 777177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 194245
25.0%
t 97113
12.5%
p 97059
12.5%
c 97051
12.5%
l 97051
12.5%
o 97049
12.5%
m 97041
12.5%
s 146
 
< 0.1%
d 111
 
< 0.1%
i 96
 
< 0.1%
Other values (7) 215
 
< 0.1%
Distinct1301
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-04 00:00:00
Maximum2018-02-23 00:00:00
2024-08-30T08:58:27.386176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:27.558572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1287
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-22 00:00:00
2024-08-30T08:58:27.732504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:27.904538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1288
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-22 00:00:00
2024-08-30T08:58:28.077350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:28.251875image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1300
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-04 00:00:00
Maximum2018-02-23 00:00:00
2024-08-30T08:58:28.422633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:28.593364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1287
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2013-01-02 00:00:00
Maximum2018-02-22 00:00:00
2024-08-30T08:58:28.764421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:28.952797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Number of Existing Stories
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct62
Distinct (%)0.1%
Missing1892
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean5.9407254
Minimum0
Maximum78
Zeros233
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:29.130571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile27
Maximum78
Range78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.8678258
Coefficient of variation (CV)1.4927177
Kurtosis9.5685779
Mean5.9407254
Median Absolute Deviation (MAD)1
Skewness3.0823604
Sum565913.5
Variance78.638334
MonotonicityNot monotonic
2024-08-30T08:58:29.291396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 32407
33.4%
3 27642
28.5%
4 9257
 
9.5%
1 5028
 
5.2%
5 2276
 
2.3%
6 2251
 
2.3%
7 1450
 
1.5%
8 1038
 
1.1%
9 753
 
0.8%
10 731
 
0.8%
Other values (52) 12427
 
12.8%
(Missing) 1892
 
1.9%
ValueCountFrequency (%)
0 233
 
0.2%
1 5028
 
5.2%
1.5 1
 
< 0.1%
2 32407
33.4%
2.5 2
 
< 0.1%
3 27642
28.5%
4 9257
 
9.5%
5 2276
 
2.3%
6 2251
 
2.3%
7 1450
 
1.5%
ValueCountFrequency (%)
78 1
 
< 0.1%
63 10
 
< 0.1%
62 1
 
< 0.1%
61 2
 
< 0.1%
60 11
 
< 0.1%
58 66
 
0.1%
55 2
 
< 0.1%
53 6
 
< 0.1%
52 276
0.3%
50 21
 
< 0.1%

Number of Proposed Stories
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct63
Distinct (%)0.1%
Missing1654
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean5.973324
Minimum0
Maximum78
Zeros82
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:29.452859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile27
Maximum78
Range78
Interquartile range (IQR)2

Descriptive statistics

Standard deviation8.8716531
Coefficient of variation (CV)1.4852121
Kurtosis9.6211189
Mean5.973324
Median Absolute Deviation (MAD)1
Skewness3.0891829
Sum570440.5
Variance78.706229
MonotonicityNot monotonic
2024-08-30T08:58:29.615858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 31835
32.8%
3 28030
28.9%
4 9881
 
10.2%
1 4722
 
4.9%
5 2431
 
2.5%
6 2329
 
2.4%
7 1433
 
1.5%
8 1039
 
1.1%
9 781
 
0.8%
10 718
 
0.7%
Other values (53) 12299
 
12.7%
(Missing) 1654
 
1.7%
ValueCountFrequency (%)
0 82
 
0.1%
1 4722
 
4.9%
1.5 1
 
< 0.1%
2 31835
32.8%
2.5 2
 
< 0.1%
3 28030
28.9%
4 9881
 
10.2%
5 2431
 
2.5%
6 2329
 
2.4%
7 1433
 
1.5%
ValueCountFrequency (%)
78 1
 
< 0.1%
63 13
 
< 0.1%
62 3
 
< 0.1%
61 2
 
< 0.1%
60 11
 
< 0.1%
58 66
 
0.1%
55 4
 
< 0.1%
54 1
 
< 0.1%
53 6
 
< 0.1%
52 275
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size853.9 KiB
False
84354 
True
12798 
ValueCountFrequency (%)
False 84354
86.8%
True 12798
 
13.2%
2024-08-30T08:58:29.746384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Distinct1992
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:30.018537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters971520
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)0.1%

Sample

1st row05/29/2014
2nd row10/22/2015
3rd row07/11/2014
4th row04/09/2017
5th row02/05/2018
ValueCountFrequency (%)
02/24/2018 162
 
0.2%
02/28/2018 158
 
0.2%
06/17/2017 143
 
0.1%
02/03/2018 140
 
0.1%
04/30/2018 136
 
0.1%
07/26/2015 135
 
0.1%
03/24/2017 129
 
0.1%
05/20/2017 129
 
0.1%
10/09/2016 128
 
0.1%
02/23/2018 126
 
0.1%
Other values (1982) 95766
98.6%
2024-08-30T08:58:30.435531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 221084
22.8%
/ 194276
20.0%
1 174893
18.0%
2 152707
15.7%
6 39114
 
4.0%
7 38840
 
4.0%
5 37865
 
3.9%
4 36368
 
3.7%
8 33139
 
3.4%
3 24637
 
2.5%
Other values (7) 18597
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 777104
80.0%
Other Punctuation 194276
 
20.0%
Lowercase Letter 140
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 221084
28.4%
1 174893
22.5%
2 152707
19.7%
6 39114
 
5.0%
7 38840
 
5.0%
5 37865
 
4.9%
4 36368
 
4.7%
8 33139
 
4.3%
3 24637
 
3.2%
9 18457
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
i 42
30.0%
n 28
20.0%
d 28
20.0%
e 14
 
10.0%
f 14
 
10.0%
a 14
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 194276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 971380
> 99.9%
Latin 140
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 221084
22.8%
/ 194276
20.0%
1 174893
18.0%
2 152707
15.7%
6 39114
 
4.0%
7 38840
 
4.0%
5 37865
 
3.9%
4 36368
 
3.7%
8 33139
 
3.4%
3 24637
 
2.5%
Latin
ValueCountFrequency (%)
i 42
30.0%
n 28
20.0%
d 28
20.0%
e 14
 
10.0%
f 14
 
10.0%
a 14
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 971520
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 221084
22.8%
/ 194276
20.0%
1 174893
18.0%
2 152707
15.7%
6 39114
 
4.0%
7 38840
 
4.0%
5 37865
 
3.9%
4 36368
 
3.7%
8 33139
 
3.4%
3 24637
 
2.5%
Other values (7) 18597
 
1.9%

Estimated Cost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct7924
Distinct (%)8.2%
Missing90
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.0276836 × 10-18
Minimum-0.075252115
Maximum133.08421
Zeros0
Zeros (%)0.0%
Negative85490
Negative (%)88.0%
Memory size1.5 MiB
2024-08-30T08:58:30.590025image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.075252115
5-th percentile-0.075252115
Q1-0.072205731
median-0.065095134
Q3-0.044779141
95-th percentile0.1279068
Maximum133.08421
Range133.15946
Interquartile range (IQR)0.02742659

Descriptive statistics

Standard deviation1.0000052
Coefficient of variation (CV)1.4229513 × 1017
Kurtosis7902.587
Mean7.0276836 × 10-18
Median Absolute Deviation (MAD)0.0091421967
Skewness77.300226
Sum4.8151066 × 10-13
Variance1.0000103
MonotonicityNot monotonic
2024-08-30T08:58:30.748392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.07525211457 10531
 
10.8%
-0.06509513403 3963
 
4.1%
-0.0701741322 3851
 
4.0%
-0.05493713768 3393
 
3.5%
-0.06001613586 3010
 
3.1%
-0.04477914134 2353
 
2.4%
-0.04985813951 2196
 
2.3%
-0.07220573147 2161
 
2.2%
-0.0671267333 2000
 
2.1%
-0.07322153111 1967
 
2.0%
Other values (7914) 61637
63.4%
ValueCountFrequency (%)
-0.07525211457 10531
10.8%
-0.07525109877 1
 
< 0.1%
-0.07524906718 2
 
< 0.1%
-0.07524805138 1
 
< 0.1%
-0.07524297238 4
 
< 0.1%
-0.07523281438 3
 
< 0.1%
-0.07522773538 7
 
< 0.1%
-0.07522265639 1
 
< 0.1%
-0.07521757739 1
 
< 0.1%
-0.07521249839 1
 
< 0.1%
ValueCountFrequency (%)
133.0842103 1
 
< 0.1%
106.5837085 3
< 0.1%
62.90432421 1
 
< 0.1%
57.22397265 1
 
< 0.1%
55.79372677 1
 
< 0.1%
50.71472859 1
 
< 0.1%
40.22964477 1
 
< 0.1%
39.54093262 2
< 0.1%
35.47773408 2
< 0.1%
35.402057 1
 
< 0.1%

Revised Cost
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8765
Distinct (%)9.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-1.0678144 × 10-17
Minimum-0.084131372
Maximum130.56436
Zeros0
Zeros (%)0.0%
Negative85035
Negative (%)87.5%
Memory size1.5 MiB
2024-08-30T08:58:30.900918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.084131372
5-th percentile-0.084130375
Q1-0.080344122
median-0.072171634
Q3-0.049248803
95-th percentile0.16502984
Maximum130.56436
Range130.64849
Interquartile range (IQR)0.031095319

Descriptive statistics

Standard deviation1.0000051
Coefficient of variation (CV)-9.3649713 × 1016
Kurtosis7484.1131
Mean-1.0678144 × 10-17
Median Absolute Deviation (MAD)0.0106641
Skewness75.019049
Sum-9.7516439 × 10-13
Variance1.0000103
MonotonicityNot monotonic
2024-08-30T08:58:31.059513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.08413037543 9969
 
10.3%
-0.07914814791 3503
 
3.6%
-0.07416492375 3417
 
3.5%
-0.06419847543 3078
 
3.2%
-0.06918169959 2672
 
2.8%
-0.05423202712 2175
 
2.2%
-0.05921525127 2049
 
2.1%
-0.08114143758 1969
 
2.0%
-0.08213808241 1946
 
2.0%
-0.07615821342 1716
 
1.8%
Other values (8755) 64657
66.6%
ValueCountFrequency (%)
-0.08413137207 2
 
< 0.1%
-0.08413037543 9969
10.3%
-0.0841301761 1
 
< 0.1%
-0.08412937878 2
 
< 0.1%
-0.08412638885 2
 
< 0.1%
-0.08412140562 4
 
< 0.1%
-0.08411741904 1
 
< 0.1%
-0.08411143918 1
 
< 0.1%
-0.08410645595 4
 
< 0.1%
-0.0840964895 1
 
< 0.1%
ValueCountFrequency (%)
130.5643613 1
 
< 0.1%
104.563576 3
< 0.1%
73.06407389 1
 
< 0.1%
61.70784821 1
 
< 0.1%
49.74811022 1
 
< 0.1%
44.76488606 1
 
< 0.1%
41.92444829 1
 
< 0.1%
40.40556157 1
 
< 0.1%
38.78501707 2
< 0.1%
34.79843774 2
< 0.1%
Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:31.347936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length13.092649
Min length4

Characters and Unicode

Total characters1271977
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row1 family dwelling
2nd row2 family dwelling
3rd rowapartments
4th rowoffice
5th rowretail sales
ValueCountFrequency (%)
family 42347
21.7%
dwelling 42347
21.7%
1 28903
14.8%
apartments 24388
12.5%
office 16117
 
8.2%
2 13444
 
6.9%
sales 3752
 
1.9%
retail 3703
 
1.9%
food/beverage 2674
 
1.4%
hndlng 2674
 
1.4%
Other values (144) 15015
 
7.7%
2024-08-30T08:58:31.787771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 144132
11.3%
i 113575
 
8.9%
a 108318
 
8.5%
e 107957
 
8.5%
98212
 
7.7%
f 79468
 
6.2%
n 79247
 
6.2%
m 69234
 
5.4%
t 63083
 
5.0%
d 52037
 
4.1%
Other values (30) 356714
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1124432
88.4%
Space Separator 98212
 
7.7%
Decimal Number 42438
 
3.3%
Other Punctuation 5451
 
0.4%
Uppercase Letter 1085
 
0.1%
Dash Punctuation 347
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 144132
12.8%
i 113575
10.1%
a 108318
9.6%
e 107957
9.6%
f 79468
 
7.1%
n 79247
 
7.0%
m 69234
 
6.2%
t 63083
 
5.6%
d 52037
 
4.6%
g 49431
 
4.4%
Other values (15) 257950
22.9%
Decimal Number
ValueCountFrequency (%)
1 28939
68.2%
2 13480
31.8%
6 11
 
< 0.1%
7 7
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 4617
84.7%
, 588
 
10.8%
& 160
 
2.9%
. 84
 
1.5%
' 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
98212
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1085
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 347
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1125517
88.5%
Common 146460
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 144132
12.8%
i 113575
10.1%
a 108318
9.6%
e 107957
9.6%
f 79468
 
7.1%
n 79247
 
7.0%
m 69234
 
6.2%
t 63083
 
5.6%
d 52037
 
4.6%
g 49431
 
4.4%
Other values (16) 259035
23.0%
Common
ValueCountFrequency (%)
98212
67.1%
1 28939
 
19.8%
2 13480
 
9.2%
/ 4617
 
3.2%
, 588
 
0.4%
- 347
 
0.2%
& 160
 
0.1%
. 84
 
0.1%
6 11
 
< 0.1%
7 7
 
< 0.1%
Other values (4) 15
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1271977
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 144132
11.3%
i 113575
 
8.9%
a 108318
 
8.5%
e 107957
 
8.5%
98212
 
7.7%
f 79468
 
6.2%
n 79247
 
6.2%
m 69234
 
5.4%
t 63083
 
5.0%
d 52037
 
4.1%
Other values (30) 356714
28.0%

Existing Units
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct312
Distinct (%)0.3%
Missing6971
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean15.60685
Minimum0
Maximum1907
Zeros18058
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:31.934342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile64
Maximum1907
Range1907
Interquartile range (IQR)3

Descriptive statistics

Standard deviation74.145567
Coefficient of variation (CV)4.750835
Kurtosis236.27048
Mean15.60685
Median Absolute Deviation (MAD)1
Skewness12.780574
Sum1407441.3
Variance5497.5651
MonotonicityNot monotonic
2024-08-30T08:58:32.093455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29185
30.0%
0 18058
18.6%
2 13874
14.3%
3 5725
 
5.9%
4 3403
 
3.5%
6 2984
 
3.1%
12 1426
 
1.5%
5 1199
 
1.2%
8 918
 
0.9%
7 665
 
0.7%
Other values (302) 12744
13.1%
(Missing) 6971
 
7.2%
ValueCountFrequency (%)
0 18058
18.6%
0.3 1
 
< 0.1%
1 29185
30.0%
2 13874
14.3%
3 5725
 
5.9%
4 3403
 
3.5%
5 1199
 
1.2%
6 2984
 
3.1%
7 665
 
0.7%
8 918
 
0.9%
ValueCountFrequency (%)
1907 26
< 0.1%
1732 3
 
< 0.1%
1500 35
< 0.1%
1499 1
 
< 0.1%
1186 26
< 0.1%
1010 2
 
< 0.1%
1005 10
 
< 0.1%
1004 2
 
< 0.1%
840 35
< 0.1%
754 4
 
< 0.1%
Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:32.412764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length13.135931
Min length4

Characters and Unicode

Total characters1276182
Distinct characters40
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)< 0.1%

Sample

1st row1 family dwelling
2nd row2 family dwelling
3rd rowapartments
4th rowoffice
5th rowretail sales
ValueCountFrequency (%)
family 42650
21.8%
dwelling 42650
21.8%
1 28782
14.7%
apartments 25310
13.0%
office 15839
 
8.1%
2 13868
 
7.1%
food/beverage 2907
 
1.5%
hndlng 2907
 
1.5%
sales 2907
 
1.5%
retail 2842
 
1.5%
Other values (144) 14538
 
7.4%
2024-08-30T08:58:32.882512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
l 143067
 
11.2%
i 113637
 
8.9%
e 108374
 
8.5%
a 107525
 
8.4%
98048
 
7.7%
n 80232
 
6.3%
f 79662
 
6.2%
m 70654
 
5.5%
t 62426
 
4.9%
d 53762
 
4.2%
Other values (30) 358795
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1127745
88.4%
Space Separator 98048
 
7.7%
Decimal Number 42788
 
3.4%
Other Punctuation 5670
 
0.4%
Uppercase Letter 1468
 
0.1%
Dash Punctuation 443
 
< 0.1%
Open Punctuation 10
 
< 0.1%
Close Punctuation 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 143067
12.7%
i 113637
10.1%
e 108374
9.6%
a 107525
9.5%
n 80232
 
7.1%
f 79662
 
7.1%
m 70654
 
6.3%
t 62426
 
5.5%
d 53762
 
4.8%
g 50052
 
4.4%
Other values (15) 258354
22.9%
Decimal Number
ValueCountFrequency (%)
1 28839
67.4%
2 13925
32.5%
7 13
 
< 0.1%
6 10
 
< 0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 4844
85.4%
, 499
 
8.8%
& 229
 
4.0%
. 84
 
1.5%
' 14
 
0.2%
Space Separator
ValueCountFrequency (%)
98048
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 1468
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 443
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1129213
88.5%
Common 146969
 
11.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 143067
12.7%
i 113637
10.1%
e 108374
9.6%
a 107525
9.5%
n 80232
 
7.1%
f 79662
 
7.1%
m 70654
 
6.3%
t 62426
 
5.5%
d 53762
 
4.8%
g 50052
 
4.4%
Other values (16) 259822
23.0%
Common
ValueCountFrequency (%)
98048
66.7%
1 28839
 
19.6%
2 13925
 
9.5%
/ 4844
 
3.3%
, 499
 
0.3%
- 443
 
0.3%
& 229
 
0.2%
. 84
 
0.1%
' 14
 
< 0.1%
7 13
 
< 0.1%
Other values (4) 31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1276182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 143067
 
11.2%
i 113637
 
8.9%
e 108374
 
8.5%
a 107525
 
8.4%
98048
 
7.7%
n 80232
 
6.3%
f 79662
 
6.2%
m 70654
 
5.5%
t 62426
 
4.9%
d 53762
 
4.2%
Other values (30) 358795
28.1%

Proposed Units
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct332
Distinct (%)0.4%
Missing6562
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean16.310023
Minimum0
Maximum1911
Zeros17174
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:33.537769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q34
95-th percentile70
Maximum1911
Range1911
Interquartile range (IQR)3

Descriptive statistics

Standard deviation74.840036
Coefficient of variation (CV)4.5885916
Kurtosis221.89618
Mean16.310023
Median Absolute Deviation (MAD)1
Skewness12.266222
Sum1477525
Variance5601.0309
MonotonicityNot monotonic
2024-08-30T08:58:33.701874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 29075
29.9%
0 17174
17.7%
2 14304
14.7%
3 6010
 
6.2%
4 3450
 
3.6%
6 3009
 
3.1%
12 1444
 
1.5%
5 1208
 
1.2%
8 919
 
0.9%
7 699
 
0.7%
Other values (322) 13298
13.7%
(Missing) 6562
 
6.8%
ValueCountFrequency (%)
0 17174
17.7%
1 29075
29.9%
2 14304
14.7%
3 6010
 
6.2%
4 3450
 
3.6%
5 1208
 
1.2%
6 3009
 
3.1%
7 699
 
0.7%
8 919
 
0.9%
9 677
 
0.7%
ValueCountFrequency (%)
1911 1
 
< 0.1%
1907 25
< 0.1%
1732 3
 
< 0.1%
1500 32
< 0.1%
1499 1
 
< 0.1%
1186 26
< 0.1%
1014 4
 
< 0.1%
1010 2
 
< 0.1%
1005 8
 
< 0.1%
840 33
< 0.1%

Plansets
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2.0
54489 
0.0
42643 
3.0
 
17
4.0
 
2
9000.0
 
1

Length

Max length6
Median length3
Mean length3.0000309
Min length3

Characters and Unicode

Total characters291459
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2.0
2nd row0.0
3rd row0.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 54489
56.1%
0.0 42643
43.9%
3.0 17
 
< 0.1%
4.0 2
 
< 0.1%
9000.0 1
 
< 0.1%

Length

2024-08-30T08:58:33.856626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:33.975879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 54489
56.1%
0.0 42643
43.9%
3.0 17
 
< 0.1%
4.0 2
 
< 0.1%
9000.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 139798
48.0%
. 97152
33.3%
2 54489
 
18.7%
3 17
 
< 0.1%
4 2
 
< 0.1%
9 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 194307
66.7%
Other Punctuation 97152
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139798
71.9%
2 54489
 
28.0%
3 17
 
< 0.1%
4 2
 
< 0.1%
9 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 97152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 291459
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 139798
48.0%
. 97152
33.3%
2 54489
 
18.7%
3 17
 
< 0.1%
4 2
 
< 0.1%
9 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 291459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 139798
48.0%
. 97152
33.3%
2 54489
 
18.7%
3 17
 
< 0.1%
4 2
 
< 0.1%
9 1
 
< 0.1%

Existing Construction Type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2226
Missing (%)2.3%
Memory size1.5 MiB
5.0
68610 
1.0
18109 
3.0
 
5610
2.0
 
2360
4.0
 
237

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters284778
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 68610
70.6%
1.0 18109
 
18.6%
3.0 5610
 
5.8%
2.0 2360
 
2.4%
4.0 237
 
0.2%
(Missing) 2226
 
2.3%

Length

2024-08-30T08:58:34.099189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:34.216300image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 68610
72.3%
1.0 18109
 
19.1%
3.0 5610
 
5.9%
2.0 2360
 
2.5%
4.0 237
 
0.2%

Most occurring characters

ValueCountFrequency (%)
. 94926
33.3%
0 94926
33.3%
5 68610
24.1%
1 18109
 
6.4%
3 5610
 
2.0%
2 2360
 
0.8%
4 237
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 189852
66.7%
Other Punctuation 94926
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 94926
50.0%
5 68610
36.1%
1 18109
 
9.5%
3 5610
 
3.0%
2 2360
 
1.2%
4 237
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 94926
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 284778
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 94926
33.3%
0 94926
33.3%
5 68610
24.1%
1 18109
 
6.4%
3 5610
 
2.0%
2 2360
 
0.8%
4 237
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284778
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 94926
33.3%
0 94926
33.3%
5 68610
24.1%
1 18109
 
6.4%
3 5610
 
2.0%
2 2360
 
0.8%
4 237
 
0.1%

Existing Construction Type Description
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing2226
Missing (%)2.3%
Memory size1.5 MiB
wood frame (5)
68610 
constr type 1
18109 
constr type 3
 
5610
constr type 2
 
2360
constr type 4
 
237

Length

Max length14
Median length14
Mean length13.722774
Min length13

Characters and Unicode

Total characters1302648
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwood frame (5)
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowconstr type 3
5th rowconstr type 3

Common Values

ValueCountFrequency (%)
wood frame (5) 68610
70.6%
constr type 1 18109
 
18.6%
constr type 3 5610
 
5.8%
constr type 2 2360
 
2.4%
constr type 4 237
 
0.2%
(Missing) 2226
 
2.3%

Length

2024-08-30T08:58:34.350888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:34.472175image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wood 68610
24.1%
frame 68610
24.1%
5 68610
24.1%
constr 26316
 
9.2%
type 26316
 
9.2%
1 18109
 
6.4%
3 5610
 
2.0%
2 2360
 
0.8%
4 237
 
0.1%

Most occurring characters

ValueCountFrequency (%)
189852
14.6%
o 163536
12.6%
r 94926
 
7.3%
e 94926
 
7.3%
d 68610
 
5.3%
w 68610
 
5.3%
f 68610
 
5.3%
a 68610
 
5.3%
m 68610
 
5.3%
( 68610
 
5.3%
Other values (12) 347748
26.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 880650
67.6%
Space Separator 189852
 
14.6%
Decimal Number 94926
 
7.3%
Open Punctuation 68610
 
5.3%
Close Punctuation 68610
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 163536
18.6%
r 94926
10.8%
e 94926
10.8%
d 68610
7.8%
w 68610
7.8%
f 68610
7.8%
a 68610
7.8%
m 68610
7.8%
t 52632
 
6.0%
c 26316
 
3.0%
Other values (4) 105264
12.0%
Decimal Number
ValueCountFrequency (%)
5 68610
72.3%
1 18109
 
19.1%
3 5610
 
5.9%
2 2360
 
2.5%
4 237
 
0.2%
Space Separator
ValueCountFrequency (%)
189852
100.0%
Open Punctuation
ValueCountFrequency (%)
( 68610
100.0%
Close Punctuation
ValueCountFrequency (%)
) 68610
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 880650
67.6%
Common 421998
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 163536
18.6%
r 94926
10.8%
e 94926
10.8%
d 68610
7.8%
w 68610
7.8%
f 68610
7.8%
a 68610
7.8%
m 68610
7.8%
t 52632
 
6.0%
c 26316
 
3.0%
Other values (4) 105264
12.0%
Common
ValueCountFrequency (%)
189852
45.0%
( 68610
 
16.3%
5 68610
 
16.3%
) 68610
 
16.3%
1 18109
 
4.3%
3 5610
 
1.3%
2 2360
 
0.6%
4 237
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1302648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
189852
14.6%
o 163536
12.6%
r 94926
 
7.3%
e 94926
 
7.3%
d 68610
 
5.3%
w 68610
 
5.3%
f 68610
 
5.3%
a 68610
 
5.3%
m 68610
 
5.3%
( 68610
 
5.3%
Other values (12) 347748
26.7%

Proposed Construction Type
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1811
Missing (%)1.9%
Memory size1.5 MiB
5.0
69200 
1.0
18066 
3.0
 
5579
2.0
 
2254
4.0
 
242

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters286023
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row5.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
5.0 69200
71.2%
1.0 18066
 
18.6%
3.0 5579
 
5.7%
2.0 2254
 
2.3%
4.0 242
 
0.2%
(Missing) 1811
 
1.9%

Length

2024-08-30T08:58:34.605766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:34.716006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0 69200
72.6%
1.0 18066
 
18.9%
3.0 5579
 
5.9%
2.0 2254
 
2.4%
4.0 242
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 95341
33.3%
0 95341
33.3%
5 69200
24.2%
1 18066
 
6.3%
3 5579
 
2.0%
2 2254
 
0.8%
4 242
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190682
66.7%
Other Punctuation 95341
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 95341
50.0%
5 69200
36.3%
1 18066
 
9.5%
3 5579
 
2.9%
2 2254
 
1.2%
4 242
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 95341
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 286023
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 95341
33.3%
0 95341
33.3%
5 69200
24.2%
1 18066
 
6.3%
3 5579
 
2.0%
2 2254
 
0.8%
4 242
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 286023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 95341
33.3%
0 95341
33.3%
5 69200
24.2%
1 18066
 
6.3%
3 5579
 
2.0%
2 2254
 
0.8%
4 242
 
0.1%

Proposed Construction Type Description
Categorical

HIGH CORRELATION  MISSING 

Distinct5
Distinct (%)< 0.1%
Missing1811
Missing (%)1.9%
Memory size1.5 MiB
wood frame (5)
69200 
constr type 1
18066 
constr type 3
 
5579
constr type 2
 
2254
constr type 4
 
242

Length

Max length14
Median length14
Mean length13.725816
Min length13

Characters and Unicode

Total characters1308633
Distinct characters22
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwood frame (5)
2nd rowwood frame (5)
3rd rowwood frame (5)
4th rowconstr type 3
5th rowconstr type 3

Common Values

ValueCountFrequency (%)
wood frame (5) 69200
71.2%
constr type 1 18066
 
18.6%
constr type 3 5579
 
5.7%
constr type 2 2254
 
2.3%
constr type 4 242
 
0.2%
(Missing) 1811
 
1.9%

Length

2024-08-30T08:58:34.849619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-30T08:58:34.969552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
wood 69200
24.2%
frame 69200
24.2%
5 69200
24.2%
constr 26141
 
9.1%
type 26141
 
9.1%
1 18066
 
6.3%
3 5579
 
2.0%
2 2254
 
0.8%
4 242
 
0.1%

Most occurring characters

ValueCountFrequency (%)
190682
14.6%
o 164541
12.6%
r 95341
 
7.3%
e 95341
 
7.3%
d 69200
 
5.3%
w 69200
 
5.3%
f 69200
 
5.3%
a 69200
 
5.3%
m 69200
 
5.3%
( 69200
 
5.3%
Other values (12) 347528
26.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 884210
67.6%
Space Separator 190682
 
14.6%
Decimal Number 95341
 
7.3%
Open Punctuation 69200
 
5.3%
Close Punctuation 69200
 
5.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 164541
18.6%
r 95341
10.8%
e 95341
10.8%
d 69200
7.8%
w 69200
7.8%
f 69200
7.8%
a 69200
7.8%
m 69200
7.8%
t 52282
 
5.9%
c 26141
 
3.0%
Other values (4) 104564
11.8%
Decimal Number
ValueCountFrequency (%)
5 69200
72.6%
1 18066
 
18.9%
3 5579
 
5.9%
2 2254
 
2.4%
4 242
 
0.3%
Space Separator
ValueCountFrequency (%)
190682
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69200
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 884210
67.6%
Common 424423
32.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 164541
18.6%
r 95341
10.8%
e 95341
10.8%
d 69200
7.8%
w 69200
7.8%
f 69200
7.8%
a 69200
7.8%
m 69200
7.8%
t 52282
 
5.9%
c 26141
 
3.0%
Other values (4) 104564
11.8%
Common
ValueCountFrequency (%)
190682
44.9%
( 69200
 
16.3%
5 69200
 
16.3%
) 69200
 
16.3%
1 18066
 
4.3%
3 5579
 
1.3%
2 2254
 
0.5%
4 242
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1308633
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
190682
14.6%
o 164541
12.6%
r 95341
 
7.3%
e 95341
 
7.3%
d 69200
 
5.3%
w 69200
 
5.3%
f 69200
 
5.3%
a 69200
 
5.3%
m 69200
 
5.3%
( 69200
 
5.3%
Other values (12) 347528
26.6%

Supervisor District
Unsupported

REJECTED  UNSUPPORTED 

Missing745
Missing (%)0.8%
Memory size1.5 MiB

Neighborhoods - Analysis Boundaries
Categorical

HIGH CORRELATION 

Distinct41
Distinct (%)< 0.1%
Missing746
Missing (%)0.8%
Memory size1.5 MiB
Financial District/South Beach
13101 
Mission
6440 
Sunset/Parkside
 
5692
West of Twin Peaks
 
4963
Castro/Upper Market
 
4015
Other values (36)
62195 

Length

Max length30
Median length18
Mean length15.325768
Min length6

Characters and Unicode

Total characters1477496
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunset/Parkside
2nd rowPacific Heights
3rd rowCastro/Upper Market
4th rowFinancial District/South Beach
5th rowChinatown

Common Values

ValueCountFrequency (%)
Financial District/South Beach 13101
 
13.5%
Mission 6440
 
6.6%
Sunset/Parkside 5692
 
5.9%
West of Twin Peaks 4963
 
5.1%
Castro/Upper Market 4015
 
4.1%
Outer Richmond 3890
 
4.0%
Marina 3689
 
3.8%
South of Market 3665
 
3.8%
Noe Valley 3644
 
3.8%
Pacific Heights 3502
 
3.6%
Other values (31) 43805
45.1%

Length

2024-08-30T08:58:35.118338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
beach 14909
 
7.6%
financial 13101
 
6.6%
district/south 13101
 
6.6%
mission 8740
 
4.4%
of 8628
 
4.4%
heights 8240
 
4.2%
market 7680
 
3.9%
hill 6768
 
3.4%
valley 6471
 
3.3%
richmond 5912
 
3.0%
Other values (46) 103465
52.5%

Most occurring characters

ValueCountFrequency (%)
i 142454
 
9.6%
e 117518
 
8.0%
a 105040
 
7.1%
t 102051
 
6.9%
n 101490
 
6.9%
100609
 
6.8%
s 87954
 
6.0%
o 74432
 
5.0%
r 74020
 
5.0%
c 59334
 
4.0%
Other values (36) 512594
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1130576
76.5%
Uppercase Letter 219067
 
14.8%
Space Separator 100609
 
6.8%
Other Punctuation 27244
 
1.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 142454
12.6%
e 117518
10.4%
a 105040
9.3%
t 102051
9.0%
n 101490
9.0%
s 87954
7.8%
o 74432
 
6.6%
r 74020
 
6.5%
c 59334
 
5.2%
h 55371
 
4.9%
Other values (13) 210912
18.7%
Uppercase Letter
ValueCountFrequency (%)
S 27532
12.6%
P 23596
10.8%
M 23198
10.6%
H 22571
10.3%
B 21570
9.8%
F 14811
 
6.8%
D 13101
 
6.0%
R 8167
 
3.7%
T 8071
 
3.7%
N 7937
 
3.6%
Other values (11) 48513
22.1%
Space Separator
ValueCountFrequency (%)
100609
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 27244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1349643
91.3%
Common 127853
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 142454
 
10.6%
e 117518
 
8.7%
a 105040
 
7.8%
t 102051
 
7.6%
n 101490
 
7.5%
s 87954
 
6.5%
o 74432
 
5.5%
r 74020
 
5.5%
c 59334
 
4.4%
h 55371
 
4.1%
Other values (34) 429979
31.9%
Common
ValueCountFrequency (%)
100609
78.7%
/ 27244
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1477496
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 142454
 
9.6%
e 117518
 
8.0%
a 105040
 
7.1%
t 102051
 
6.9%
n 101490
 
6.9%
100609
 
6.8%
s 87954
 
6.0%
o 74432
 
5.0%
r 74020
 
5.0%
c 59334
 
4.0%
Other values (36) 512594
34.7%

Zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)< 0.1%
Missing744
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean94115.436
Minimum94102
Maximum94158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:35.246027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum94102
5-th percentile94103
Q194109
median94114
Q394122
95-th percentile94133
Maximum94158
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.354154
Coefficient of variation (CV)9.9390221 × 10-5
Kurtosis1.2429194
Mean94115.436
Median Absolute Deviation (MAD)7
Skewness0.86801113
Sum9.073481 × 109
Variance87.500196
MonotonicityNot monotonic
2024-08-30T08:58:35.384455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
94110 8259
 
8.5%
94114 6172
 
6.4%
94117 5347
 
5.5%
94105 5265
 
5.4%
94103 5084
 
5.2%
94109 4864
 
5.0%
94122 4603
 
4.7%
94115 4516
 
4.6%
94118 4441
 
4.6%
94123 4266
 
4.4%
Other values (17) 43591
44.9%
ValueCountFrequency (%)
94102 3235
 
3.3%
94103 5084
5.2%
94104 2755
 
2.8%
94105 5265
5.4%
94107 3921
4.0%
94108 2476
 
2.5%
94109 4864
5.0%
94110 8259
8.5%
94111 3213
 
3.3%
94112 4101
4.2%
ValueCountFrequency (%)
94158 549
 
0.6%
94134 1497
 
1.5%
94133 3151
3.2%
94132 1948
2.0%
94131 3948
4.1%
94130 31
 
< 0.1%
94129 7
 
< 0.1%
94127 2892
3.0%
94124 2338
2.4%
94123 4266
4.4%
Distinct37119
Distinct (%)38.5%
Missing735
Missing (%)0.8%
Memory size1.5 MiB
2024-08-30T08:58:35.665958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length41
Median length40
Mean length40.042337
Min length35

Characters and Unicode

Total characters3860762
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20266 ?
Unique (%)21.0%

Sample

1st row(37.759041020475465, -122.50286985467523)
2nd row(37.78762264983362, -122.43099126735969)
3rd row(37.76416871595274, -122.43039745629406)
4th row(37.78992343288329, -122.3915399961996)
5th row(37.79621627942539, -122.40375479881872)
ValueCountFrequency (%)
37.79226164705184 375
 
0.2%
122.4034859571375 375
 
0.2%
37.79294896659241 228
 
0.1%
122.39809861435491 228
 
0.1%
37.78377017210105 180
 
0.1%
122.43195970140347 180
 
0.1%
122.47676641508518 177
 
0.1%
37.728556952954136 177
 
0.1%
37.793357219595194 171
 
0.1%
122.39421470312315 171
 
0.1%
Other values (74220) 190572
98.8%
2024-08-30T08:58:36.084154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 439465
11.4%
7 436673
11.3%
3 361250
9.4%
1 343725
8.9%
4 339738
8.8%
5 262047
 
6.8%
9 256732
 
6.6%
8 256653
 
6.6%
6 254448
 
6.6%
0 235112
 
6.1%
Other values (6) 674919
17.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3185843
82.5%
Other Punctuation 289251
 
7.5%
Open Punctuation 96417
 
2.5%
Space Separator 96417
 
2.5%
Dash Punctuation 96417
 
2.5%
Close Punctuation 96417
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 439465
13.8%
7 436673
13.7%
3 361250
11.3%
1 343725
10.8%
4 339738
10.7%
5 262047
8.2%
9 256732
8.1%
8 256653
8.1%
6 254448
8.0%
0 235112
7.4%
Other Punctuation
ValueCountFrequency (%)
. 192834
66.7%
, 96417
33.3%
Open Punctuation
ValueCountFrequency (%)
( 96417
100.0%
Space Separator
ValueCountFrequency (%)
96417
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 96417
100.0%
Close Punctuation
ValueCountFrequency (%)
) 96417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3860762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 439465
11.4%
7 436673
11.3%
3 361250
9.4%
1 343725
8.9%
4 339738
8.8%
5 262047
 
6.8%
9 256732
 
6.6%
8 256653
 
6.6%
6 254448
 
6.6%
0 235112
 
6.1%
Other values (6) 674919
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3860762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 439465
11.4%
7 436673
11.3%
3 361250
9.4%
1 343725
8.9%
4 339738
8.8%
5 262047
 
6.8%
9 256732
 
6.6%
8 256653
 
6.6%
6 254448
 
6.6%
0 235112
 
6.1%
Other values (6) 674919
17.5%

Record ID
Real number (ℝ)

Distinct97148
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1574893 × 1012
Minimum1.2935322 × 1010
Maximum1.4981672 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-08-30T08:58:36.234053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.2935322 × 1010
5-th percentile1.3371992 × 1011
Q11.3062539 × 1012
median1.3585908 × 1012
Q31.4152727 × 1012
95-th percentile1.4693428 × 1012
Maximum1.4981672 × 1012
Range1.4852319 × 1012
Interquartile range (IQR)1.0901874 × 1011

Descriptive statistics

Standard deviation4.8075057 × 1011
Coefficient of variation (CV)0.4153391
Kurtosis0.71685322
Mean1.1574893 × 1012
Median Absolute Deviation (MAD)5.4266169 × 1010
Skewness-1.6260545
Sum1.124524 × 1017
Variance2.3112111 × 1023
MonotonicityNot monotonic
2024-08-30T08:58:36.396981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.344366188 × 10122
 
< 0.1%
1.414026241 × 10122
 
< 0.1%
1.350685167 × 10122
 
< 0.1%
1.301037451 × 10122
 
< 0.1%
1.339291367 × 10121
 
< 0.1%
1.365494153 × 10121
 
< 0.1%
1.471852133 × 10121
 
< 0.1%
1.336888489 × 10121
 
< 0.1%
1.406675165 × 10121
 
< 0.1%
1.404037288 × 10121
 
< 0.1%
Other values (97138) 97138
> 99.9%
ValueCountFrequency (%)
1.29353215 × 10101
< 0.1%
1.295722223 × 10101
< 0.1%
1.296226196 × 10101
< 0.1%
1.296629102 × 10101
< 0.1%
1.297668221 × 10101
< 0.1%
1.298230162 × 10101
< 0.1%
1.299548187 × 10101
< 0.1%
1.302453234 × 10101
< 0.1%
1.3025251 × 10101
< 0.1%
1.3025301 × 10101
< 0.1%
ValueCountFrequency (%)
1.498167208 × 10121
< 0.1%
1.497918511 × 10121
< 0.1%
1.497908141 × 10121
< 0.1%
1.497865153 × 10121
< 0.1%
1.497759172 × 10121
< 0.1%
1.497639217 × 10121
< 0.1%
1.497595146 × 10121
< 0.1%
1.497593146 × 10121
< 0.1%
1.497577112 × 10121
< 0.1%
1.497564467 × 10121
< 0.1%

Interactions

2024-08-30T08:58:16.824641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:00.662283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.163629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.502923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.832086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.351921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.932719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.739880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.240166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.827100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.347391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.958582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:00.797766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.288936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.624118image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.964687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.504730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:09.057909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.886415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.373119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.962975image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.485850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.076269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:00.917800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.390292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.735282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.083745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.629637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:09.494237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.999280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.493078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.087232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.604673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.207156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:01.052197image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.506295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.851956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.214235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.763205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:09.623929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.124313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.619010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.216735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.736368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.350102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.222848image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.639474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.971842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.354003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.908383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:09.763504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.260234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.764652image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.361655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.873785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.492578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.355493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.766434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.091187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.491199image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.057866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:09.906230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.450521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.909078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.511012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.005234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.628331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.483784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.889516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.206585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.620388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.211131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.028009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.576146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.047973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.644754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.141744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.760243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.609420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.007913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.319976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.750736image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.345954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.145167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.696718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.215272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.780819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.273978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:17.919080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.742325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.134376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.433651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:06.914250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.489867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.267854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.835510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.375214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:14.920519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.418020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:18.093468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:02.881207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.260951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.550714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.077806image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.636909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.395014image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:11.972995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.524583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.061970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.556769image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:18.243896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:03.021354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:04.383291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:05.694509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:07.209647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:08.780418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:10.572660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:12.101949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:13.676748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:15.204653image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-08-30T08:58:16.685879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-08-30T08:58:36.531194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Current StatusEstimated CostExisting Construction TypeExisting Construction Type DescriptionExisting UnitsFire Only PermitNeighborhoods - Analysis BoundariesNumber of Existing StoriesNumber of Proposed StoriesPermit TypePermit Type DefinitionPlansetsProposed Construction TypeProposed Construction Type DescriptionProposed UnitsRecord IDRevised CostStreet NumberStreet SuffixUnitZipcode
Current Status1.0000.0000.0000.0000.0000.0040.0180.0000.0000.0110.0110.0000.0000.0000.0000.0130.0000.0000.0000.0000.002
Estimated Cost0.0001.0000.0070.007-0.0400.0050.0160.1140.114-0.1450.2350.0330.0130.013-0.046-0.0310.958-0.0380.0180.031-0.042
Existing Construction Type0.0000.0071.0001.0000.1410.4320.4380.4110.4140.0600.0600.1970.9950.9950.1400.0920.0060.1260.1540.1330.310
Existing Construction Type Description0.0000.0071.0001.0000.1410.4320.4380.4110.4140.0600.0600.1970.9950.9950.1400.0920.0060.1260.1540.1330.310
Existing Units0.000-0.0400.1410.1411.0000.0750.1380.1510.1370.0240.0120.0270.1420.1420.9850.030-0.0360.1800.0720.4660.062
Fire Only Permit0.0040.0050.4320.4320.0751.0000.4140.3700.3620.0500.0500.3370.4290.4290.0750.0640.0000.1230.1490.0320.309
Neighborhoods - Analysis Boundaries0.0180.0160.4380.4380.1380.4141.0000.2800.2820.1450.1450.1940.4340.4340.1360.3650.0160.3180.2200.1140.774
Number of Existing Stories0.0000.1140.4110.4110.1510.3700.2801.0000.9880.0000.0260.1490.4130.4130.146-0.1200.117-0.1430.0940.191-0.426
Number of Proposed Stories0.0000.1140.4140.4140.1370.3620.2820.9881.000-0.0280.0290.1490.4130.4130.157-0.1210.120-0.1410.0950.189-0.429
Permit Type0.011-0.1450.0600.0600.0240.0500.1450.000-0.0281.0001.0000.1160.0360.036-0.0320.066-0.168-0.0170.1000.0500.004
Permit Type Definition0.0110.2350.0600.0600.0120.0500.1450.0260.0291.0001.0000.1160.0360.0360.0090.0490.2320.0200.1000.0000.046
Plansets0.0000.0330.1970.1970.0270.3370.1940.1490.1490.1160.1161.0000.1950.1950.0290.0570.0340.0420.0830.0280.148
Proposed Construction Type0.0000.0130.9950.9950.1420.4290.4340.4130.4130.0360.0360.1951.0001.0000.1400.0910.0150.1250.1550.1330.312
Proposed Construction Type Description0.0000.0130.9950.9950.1420.4290.4340.4130.4130.0360.0360.1951.0001.0000.1400.0910.0150.1250.1550.1330.312
Proposed Units0.000-0.0460.1400.1400.9850.0750.1360.1460.157-0.0320.0090.0290.1400.1401.0000.027-0.0400.1790.0760.4640.049
Record ID0.013-0.0310.0920.0920.0300.0640.365-0.120-0.1210.0660.0490.0570.0910.0910.0271.000-0.032-0.0160.105-0.2370.014
Revised Cost0.0000.9580.0060.006-0.0360.0000.0160.1170.120-0.1680.2320.0340.0150.015-0.040-0.0321.000-0.0360.0150.020-0.047
Street Number0.000-0.0380.1260.1260.1800.1230.318-0.143-0.141-0.0170.0200.0420.1250.1250.179-0.016-0.0361.0000.1180.0190.160
Street Suffix0.0000.0180.1540.1540.0720.1490.2200.0940.0950.1000.1000.0830.1550.1550.0760.1050.0150.1181.0000.0800.254
Unit0.0000.0310.1330.1330.4660.0320.1140.1910.1890.0500.0000.0280.1330.1330.464-0.2370.0200.0190.0801.000-0.022
Zipcode0.002-0.0420.3100.3100.0620.3090.774-0.426-0.4290.0040.0460.1480.3120.3120.0490.014-0.0470.1600.254-0.0221.000

Missing values

2024-08-30T08:58:18.970402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-30T08:58:19.818431image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-30T08:58:20.706252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet NameStreet SuffixUnitDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateNumber of Existing StoriesNumber of Proposed StoriesFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
12013053183568otc alterations permit05/31/20131810017A148343rDAvNaNremodel kitchen: replace countertop, cabinets, sink, stove, hardwood floor & lighting.complete08/28/201305/31/201306/03/201308/28/201306/03/20132.02.0N05/29/2014-0.063064-0.0721721 family dwelling1.01 family dwelling1.02.05.0wood frame (5)5.0wood frame (5)4.0Sunset/Parkside94122.0(37.759041020475465, -122.50286985467523)1306559115258
32014102799838otc alterations permit10/27/201406610052020bUsHStNaNtwo kitchens & two bathrooms remodel. replace kitchen cabinets, countertops & appliances in same location. replace tub & faucet & vanity & tile in same location in both units.complete12/31/201410/27/201410/27/201412/31/201410/27/20142.02.0N10/22/2015-0.039700-0.0492492 family dwelling2.02 family dwelling2.00.05.0wood frame (5)5.0wood frame (5)5.0Pacific Heights94115.0(37.78762264983362, -122.43099126735969)136037778128
52013071619258otc alterations permit07/16/20133565076347916tHStNaNto comply with physical inspection report #cc-7260 item #1 -all storage items are being removed.complete10/08/201307/16/201307/16/201310/08/201307/16/20133.03.0N07/11/2014-0.067635-0.076657apartments3.0apartments3.00.05.0wood frame (5)5.0wood frame (5)8.0Castro/Upper Market94114.0(37.76416871595274, -122.43039745629406)1311167285194
82014041130258otc alterations permit04/11/20143740029126fOlSoMStNaNexisting office and storage facility. new partitions, restroom upgrade. electrical on a separate permitcomplete10/02/201404/11/201404/25/201410/02/201404/25/20141.01.0N04/09/20170.0517220.040449officeNaNofficeNaN2.03.0constr type 33.0constr type 36.0Financial District/South Beach94105.0(37.78992343288329, -122.3915399961996)1338371165676
92017021091348otc alterations permit02/10/20170195001735mOnTgOmErYStNaNbasement & 1/f- as built drawing for fire/smoke damper on hvac duct (existing) under pa#201612074341. n/a ordinance #155-13complete03/09/201702/10/201702/10/201703/09/201702/10/20174.04.0N02/05/2018-0.075252-0.084130retail sales0.0retail sales0.02.03.0constr type 33.0constr type 33.0Chinatown94111.0(37.79621627942539, -122.40375479881872)145294162223
122013013191718otc alterations permit01/31/20135683009319gAtEsStNaNto obtain final inspection for work approved under pa#200907092266 all work is complete.complete02/25/201301/31/201301/31/201302/25/201301/31/20132.02.0N01/26/2014-0.075252-0.0841301 family dwelling1.01 family dwelling1.00.05.0wood frame (5)5.0wood frame (5)9.0Bernal Heights94110.0(37.738645966485414, -122.41376867233384)1294812186759
132017032723748otc alterations permit03/27/20175254016A1586hUdSoNAvNaNreroofing - n/a mahercomplete03/31/201703/27/201703/27/201703/31/201703/27/20173.03.0N03/22/2018-0.062302-0.0714242 family dwelling2.02 family dwelling2.00.05.0wood frame (5)5.0wood frame (5)10.0Bayview Hunters Point94124.0(37.74009281621103, -122.38782062796065)1457402178140
142013061394968otc alterations permit06/13/20133708058575mArKeTStNaNrelocate (4) sprinklers per t.i, 38th fl. t.i ref pa# 201305298105.complete07/01/201306/13/201306/17/201307/01/201306/17/201340.040.0Y06/12/2014-0.073165-0.082082officeNaNofficeNaN2.01.0constr type 11.0constr type 16.0Financial District/South Beach94105.0(37.789550846009895, -122.40035964704002)1307996164536
152015072826568otc alterations permit07/28/20150274016995pInEStNaNunit 205: replace kitchen counter tops and cabinets. replace bathtub, vanity and tiles in bathroom.complete10/15/201507/28/201507/28/201510/15/201507/28/20155.05.0N07/22/2016-0.056969-0.066192apartments29.0apartments29.00.05.0wood frame (5)5.0wood frame (5)3.0Nob Hill94108.0(37.79048343152098, -122.41201759718454)1389955233429
162013091770454sign - erect09/17/201315170376333gEaRyBlNaNerect electric signcomplete11/19/201309/17/201309/17/201311/19/201309/17/20131.0NaNN09/12/2014-0.068650-0.077653retail sales0.0No definidoNaN2.03.0constr type 3NaNNaN1.0Outer Richmond94121.0(37.779635697353044, -122.48739021723274)1317862102479
Permit NumberPermit TypePermit Type DefinitionPermit Creation DateBlockLotStreet NumberStreet NameStreet SuffixUnitDescriptionCurrent StatusCurrent Status DateFiled DateIssued DateCompleted DateFirst Construction Document DateNumber of Existing StoriesNumber of Proposed StoriesFire Only PermitPermit Expiration DateEstimated CostRevised CostExisting UseExisting UnitsProposed UseProposed UnitsPlansetsExisting Construction TypeExisting Construction Type DescriptionProposed Construction TypeProposed Construction Type DescriptionSupervisor DistrictNeighborhoods - Analysis BoundariesZipcodeLocationRecord ID
1988872013022609628otc alterations permit02/26/201309100033741bRoDeRiCkStNaNreroofingcomplete03/25/201302/26/201302/26/201303/25/201302/26/20133.03.0N02/21/2014-0.059405-0.0685821 family dwelling1.01 family dwelling1.00.05.0wood frame (5)5.0wood frame (5)2.0Marina94123.0(37.80496756184145, -122.4458427365375)129714583806
1988922014102093158otc alterations permit10/20/201411580221884mCaLlIsTeRStNaNremove 7 (e) windows on 2/f & 3/f at rear of building to be replaced with 2 new raised panel glass windows & 2 (n) fixed windows.complete12/04/201410/20/201410/31/201412/04/201410/31/20143.03.0N10/26/2015-0.070174-0.064198apartments4.0apartments4.02.05.0wood frame (5)5.0wood frame (5)5.0Lone Mountain/USF94115.0(37.777413174246156, -122.44320680410473)1359485463242
1988932014111212178otc alterations permit11/12/20140328001760mArKeTStNaN7th fl: electrical, lighting, power & signal t.i. pa# 201409176549. n/a ordinance #155-13complete09/04/201511/12/201411/14/201409/04/201511/14/201411.011.0N10/29/20170.5833960.562098office0.0office0.02.01.0constr type 11.0constr type 13.0Financial District/South Beach94108.0(37.786382767276045, -122.40546332975441)136200169778
1988942014030601098otc alterations permit03/06/20143534077254cLiNtOnPk0.0install fiber cement siding materials on front elevation and entry way; fiberdemen6t window trim included. u-factor not more than .32complete12/16/201403/06/201403/06/201412/16/201403/06/20142.02.0N03/01/2015-0.064587-0.0736672 family dwelling2.02 family dwelling2.00.05.0wood frame (5)5.0wood frame (5)8.0Mission94103.0(37.76920156531753, -122.42572939292191)1334652472658
1988972017030104028otc alterations permit03/01/201754130381623wAlLaCeAvNaNabate nov 201651391 - repair fire & water damage, replacement of ceiling, walls and floor finishes down to rough framing. no layout & framing changes. remodel kitchen and bath on upper level, approx 800 sf of area in repair. work only on legal installations. replace 5 rear windows. other work undercomplete10/31/201703/01/201703/01/201710/31/201703/01/20172.02.0N02/24/2018-0.004147-0.0143661 family dwelling1.01 family dwelling1.00.05.0wood frame (5)5.0wood frame (5)10.0Bayview Hunters Point94124.0(37.72754415299655, -122.39176691964768)1454702182099
1988982016060188158otc alterations permit06/01/20163561086295cAsTrOStNaNremodel #293 at 2/f, remodel (e) bathrooms, relocate kitchen, add washer & dryer, alter one bearing wall.complete10/13/201606/01/201606/06/201610/13/201606/06/20164.04.0N06/01/2017-0.0244630.010550apartments10.0apartments10.02.05.0wood frame (5)5.0wood frame (5)8.0Castro/Upper Market94114.0(37.76430617710508, -122.43502013192992)1425631158737
1989002015060277678otc alterations permit06/02/20157101A007736hUrOnAvNaNreroofingcomplete03/11/201606/02/201506/02/201503/11/201606/02/20152.02.0N05/27/2016-0.069158-0.0781521 family dwelling1.01 family dwelling1.00.05.0wood frame (5)5.0wood frame (5)11.0Outer Mission94112.0(37.71192805393076, -122.4500944316179)1383549221502
1989022016092081758otc alterations permit09/20/20162130A006G78cRaGmOnTAvNaNreplace (e) tub with walk in tub, 20 amp circuit, gfci outletcomplete09/28/201609/20/201609/20/201609/28/201609/20/20162.02.0N09/15/2017-0.057070-0.0662911 family dwelling1.01 family dwelling1.00.05.0wood frame (5)5.0wood frame (5)7.0Inner Sunset94116.0(37.74973733662819, -122.4671301493082)1438114126978
1989032014071614168otc alterations permit07/16/2014252802466cReStLaKeDrNaNcut a door and a staircase for access to backyard.build deck next to staircase.complete01/23/201507/16/201410/28/201401/23/201510/28/20142.02.0N10/23/2015-0.070682-0.0781521 family dwelling1.01 family dwelling1.02.05.0wood frame (5)5.0wood frame (5)4.0Sunset/Parkside94132.0(37.73542953855242, -122.48149902038024)1349089138746
1989052016040539583additions alterations or repairs04/05/20167295021325120tHAvNaNti for space 129,130, 132 partitions, mep, shelving, stonestown mall. ab -56 compliance 2014-0627-9771complete11/22/201604/05/201607/18/201611/22/201607/18/20162.02.0N07/03/20190.2548820.840764retail sales0.0retail sales0.02.02.0constr type 22.0constr type 27.0Lakeshore94132.0(37.728556952954136, -122.47676641508518)1418495226171